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Books on the topic 'Cargo weight'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Mikhailov, Gennadii A. Optimization of Weighted Monte Carlo Methods. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-75981-9.

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2

Mikhaĭlov, G. A. Optimization of weighted Monte Carlo methods. Springer-Verlag, 1992.

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3

Tex.) International Conference on Weigh-in-Motion (6th 2012 Dallas. International Conference on Weigh-in-Motion: ICWIM 6. ISTE, 2012.

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4

Indonesia. Direktorat Jenderal Perdagangan Dalam Negeri., ed. Kompilasi peraturan kebijakan di bidang perdagangan dalam negeri. Direktorat Jenderah Perdagangan Dalam Negeri, Departemen Perdagangan, 2007.

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5

Schwenzfeger, K. J. Comparison of ERS-1 scatterometer Monte Carlo performance simulations using a weighted nonlinear least-squares and a maximum likelihood estimation method. Hochschule der Bundeswehr München, 1985.

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6

Tull, Malcolm. The Shipping of Fremantle. Liverpool University Press, 2018. http://dx.doi.org/10.5949/liverpool/9780968128824.003.0005.

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This chapter discusses the development of port activity to suit the movement of ships. Over the course of the twentieth century the average vessel size grew from 5200 to 19,000 gross registered tons. The development in ship technology changed the nature, shape and weight of cargo and passenger ships significantly enough to require a vast restructuring of the port to include deep-water channels and berths - a costly endeavour.

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7

Kockott, Götz, and Gennadii A. Mikhailov. Optimization of Weighted Monte Carlo Methods. Brand: Springer, 2011.

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8

Allen, Michael P., and Dominic J. Tildesley. Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0004.

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The estimation of integrals by Monte Carlo sampling is introduced through a simple example. The chapter then explains importance sampling, and the use of the Metropolis and Barker forms of the transition matrix defined in terms of the underlying matrix of the Markov chain. The creation of an appropriately weighted set of states in the canonical ensemble is described in detail and the method is extended to the isothermal–isobaric, grand canonical and semi-grand ensembles. The Monte Carlo simulation of molecular fluids and fluids containing flexible molecules using a reptation algorithm is discu

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9

Allen, Michael P., and Dominic J. Tildesley. Advanced Monte Carlo methods. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0009.

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This chapter describes the ways in which the Monte Carlo importance sampling method may be adapted to improve the calculation of ensemble averages, particularly those associated with free energy differences. These approaches include umbrella sampling, non-Boltzmann sampling, the Wang–Landau method, and nested sampling. In addition, a range of special techniques have been developed to accelerate the simulation of flexible molecules, such as polymers. These approaches are illustrated with scientific examples and program code. The chapter also explains the analysis of such simulations using techn

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10

Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.

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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of

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11

Rondinone, Troy. “And the Winner—Television!”. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037375.003.0002.

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This chapter traces boxing's crooked path to respectability in order to gain an understanding the incredible success of TV boxing in the middle of the twentieth century. Boxing was born in the late 1800s, when the adoption of the Queensberry rules ended the old bare-knuckle days of limitless rounds, neck choking, no weight classifications, and muddy deaths. The new rules imposed uniformly sized rings, three-minute rounds, standardized judging, weight categories, and padded leather boxing gloves. The new structure fit in well with Progressive Era concerns regarding social regulation and masculi

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12

Value-Focused Thinking in the Presence of Weight Ambiguity: A Solution Technique Using Monte Carlo Simulation. Storming Media, 2004.

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13

Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

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Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions o

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14

Salicone, Simona. Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence. Springer, 2010.

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15

Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence (Springer Series in Reliability Engineering). Springer, 2006.

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16

Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence. Springer, 2007.

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